



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class      _UpperCAmelCase      (							__lowerCAmelCase  ):
     UpperCamelCase__		     =					(EulerDiscreteScheduler,)
     UpperCamelCase__		     =					10




     def 		snake_case_		(					self			,    **a__):
         A__					=       {
             '''num_train_timesteps''': 1_1_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**lowerCamelCase_)
         return config




     def 		snake_case_		(					self):


         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=lowerCamelCase_)




     def 		snake_case_		(					self):


         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,    [0.0_0_0_2, 0.0_0_2, 0.0_2]):
             self.check_over_configs(beta_start=lowerCamelCase_			,    beta_end=lowerCamelCase_)




     def 		snake_case_		(					self):


         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=lowerCamelCase_)




     def 		snake_case_		(					self):


         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=lowerCamelCase_)




     def 		snake_case_		(					self):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config()
         A__					=       scheduler_class(**lowerCamelCase_)

         scheduler.set_timesteps(self.num_inference_steps)

         A__					=       torch.manual_seed(0)

         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter * scheduler.init_noise_sigma
         A__					=       sample.to(lowerCamelCase_)

         for i, t in enumerate(scheduler.timesteps):
             A__					=       scheduler.scale_model_input(lowerCamelCase_			,    lowerCamelCase_)

             A__					=       model(lowerCamelCase_			,    lowerCamelCase_)

             A__					=       scheduler.step(lowerCamelCase_			,    lowerCamelCase_			,    lowerCamelCase_			,    generator=lowerCamelCase_)
             A__					=       output.prev_sample

         A__					=       torch.sum(torch.abs(lowerCamelCase_))
         A__					=       torch.mean(torch.abs(lowerCamelCase_))

         assert abs(result_sum.item() - 1_0.0_8_0_7) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1) < 1e-3




     def 		snake_case_		(					self):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config(prediction_type='''v_prediction''')
         A__					=       scheduler_class(**lowerCamelCase_)

         scheduler.set_timesteps(self.num_inference_steps)

         A__					=       torch.manual_seed(0)

         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter * scheduler.init_noise_sigma
         A__					=       sample.to(lowerCamelCase_)

         for i, t in enumerate(scheduler.timesteps):
             A__					=       scheduler.scale_model_input(lowerCamelCase_			,    lowerCamelCase_)

             A__					=       model(lowerCamelCase_			,    lowerCamelCase_)

             A__					=       scheduler.step(lowerCamelCase_			,    lowerCamelCase_			,    lowerCamelCase_			,    generator=lowerCamelCase_)
             A__					=       output.prev_sample

         A__					=       torch.sum(torch.abs(lowerCamelCase_))
         A__					=       torch.mean(torch.abs(lowerCamelCase_))

         assert abs(result_sum.item() - 0.0_0_0_2) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06) < 1e-3




     def 		snake_case_		(					self):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config()
         A__					=       scheduler_class(**lowerCamelCase_)

         scheduler.set_timesteps(self.num_inference_steps			,    device=lowerCamelCase_)

         A__					=       torch.manual_seed(0)

         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         A__					=       sample.to(lowerCamelCase_)

         for t in scheduler.timesteps:
             A__					=       scheduler.scale_model_input(lowerCamelCase_			,    lowerCamelCase_)

             A__					=       model(lowerCamelCase_			,    lowerCamelCase_)

             A__					=       scheduler.step(lowerCamelCase_			,    lowerCamelCase_			,    lowerCamelCase_			,    generator=lowerCamelCase_)
             A__					=       output.prev_sample

         A__					=       torch.sum(torch.abs(lowerCamelCase_))
         A__					=       torch.mean(torch.abs(lowerCamelCase_))

         assert abs(result_sum.item() - 1_0.0_8_0_7) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1) < 1e-3




     def 		snake_case_		(					self):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config()
         A__					=       scheduler_class(**lowerCamelCase_			,    use_karras_sigmas=lowerCamelCase_)

         scheduler.set_timesteps(self.num_inference_steps			,    device=lowerCamelCase_)

         A__					=       torch.manual_seed(0)

         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         A__					=       sample.to(lowerCamelCase_)

         for t in scheduler.timesteps:
             A__					=       scheduler.scale_model_input(lowerCamelCase_			,    lowerCamelCase_)

             A__					=       model(lowerCamelCase_			,    lowerCamelCase_)

             A__					=       scheduler.step(lowerCamelCase_			,    lowerCamelCase_			,    lowerCamelCase_			,    generator=lowerCamelCase_)
             A__					=       output.prev_sample

         A__					=       torch.sum(torch.abs(lowerCamelCase_))
         A__					=       torch.mean(torch.abs(lowerCamelCase_))

         assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1e-2
         assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1e-3

def 			lowercase(	UpperCamelCase_ = 100       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      n * (n + 1) * (2 * n + 1) / 6
 UpperCamelCase			  =      (n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares       )


if __name__ == "__main__":
     print(F'''{solution() = }''')
